{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_cumulative_gain method\n",
    "\n",
    "In this example, we'll be plotting a cumulative gain curve to describe the classification performance of a `LogisticRegression` classifier using the breast cncer dataset from scikit-learn. Here, we'll be using the `scikitplot.metrics.plot_cumulative_gain` method.\n",
    "A cumulative gain plot is a method of evaluating the effectiveness of a classification model by visualising the gain in true positives for a given fraction of the total population targeted by the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\"\"\"\n",
    "An example showing the plot_cumulative_gain method used\n",
    "by a scikit-learn classifier\n",
    "\"\"\"\n",
    "from __future__ import absolute_import\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_breast_cancer as load_data\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create an instance of the LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# Perform predictions\n",
    "lr.fit(X, y)\n",
    "probas = lr.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5c82b5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "skplt.metrics.plot_cumulative_gain(y_true=y, y_probas=probas)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
